DATA
Load any CSV, Excel, or JSON; profile it, clean it, group, pivot, and merge multiple sources to answer real business questions in code.
print("hello world") to Production AI Systems.Most books stop at the happy path. This one walks you into the wall — the moment your model plateaus at 0.933 and no amount of tuning moves it — and spends ten chapters teaching you what to do next.
Every technique in the book is taught twice: once on a small abstract example, then on CinemaStream's actual tables — users, movies, watch events, churn — alongside colleagues who review your pull requests and push back on your shortcuts.
You don't just read about pipelines, models, and dashboards. You build a complete runnable portfolio repo — the same one, extended chapter after chapter, until it's something you can show in an interview.
One company. One codebase. One continuous arc from your first variable to a deployed, monitored ML system.
The bar isn't "you read about it." After each module you can do the thing, cold.
Load any CSV, Excel, or JSON; profile it, clean it, group, pivot, and merge multiple sources to answer real business questions in code.
Write any query an analytics job demands — CTEs, window functions, self-joins — and read an execution plan well enough to add the right index.
Build a scheduled pipeline with retries and alerts, write data-quality tests, detect anomalies, trace lineage, and debug one that's broken at 2 a.m.
Build, tune, and evaluate a model end-to-end, deploy it behind a REST API, track experiments, and monitor for the drift that quietly kills it.
Ship a Streamlit MVP in a day, write production-grade prompts with versioning and fallbacks, and fine-tune a small transformer.
Turn a vague business ask into a technical brief, run a discovery interview, scope a PoC, and demo to a non-technical client without losing the room.
A single ordered progression. Each module ends where the next begins — no topic debuts in the capstone.
The same 180 chapters — read your way. The opening guide maps the path that fits you.
You've never written a line of code. Module 1 assumes exactly that, and the arc carries you all the way to a deployed ML system without a cliff.
You live in SQL and spreadsheets but want to build and ship. Skip the basics; the reading paths route you straight to pipelines, ML, and the FDE craft.
You can build models but deploying them inside a real organization is a different discipline. The consulting and harness modules are written for you.
INDIA at checkout for regional pricing (≈₹1,499).The FDE Mindset (the thesis) and Supervised Learning (real code, real output, the 0.933 wall). Twenty-four pages, no email required.
Yes. Module 1 assumes no prior coding. The very first chapters explain what a program is. If you can open a terminal, you can start — the book never skips a rung on the ladder.
None to begin. By the end you'll be writing production ML code. If you're already comfortable, the reading paths let you skip ahead without missing dependencies — nothing important is introduced only in passing.
Not for the first edition. At roughly 3,000 pages it would be six physical volumes, and the ebook is fully searchable across all of them — which matters more for a reference you'll return to. EPUB and PDF are included.
A bundle: EPUB (reflowable, for e-readers and phones) and PDF (fixed layout, for the desktop and printing chapters you want on paper). Both are DRM-free and yours to keep.
Every gated code block in the book is executed in a pinned container before release and reproduces byte-for-byte. The companion repo ships the same code as notebooks plus a Docker image, so your results match the page.